{"id":4066489,"url":"https://alion.io/job/apple-generative-ai-engineer-imagevideo-restoration","title":"Generative AI Engineer, Image/Video Restoration","company":{"id":12,"name":"Apple","domain":"apple.com","url":"https://alion.io/company/apple","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Apple Jobs","truth_index":{"grade":"A","score":88,"open_postings":1305,"ghost_share":0.002,"stale_share":0.113,"repost_share":0.247,"time_to_fill_p50_days":149,"computed_at":"2026-11-15T07:52:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Beijing, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Diffusion Models","optional":false},{"name":"Git","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Amazon S3","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"Quantization","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2025-09-10T10:20:16Z","employer_posted_date":"2026-11-15","last_verified_at":"2026-11-15T07:07:09Z","board_verified":true,"closed_at":null,"days_open":431,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":431},"description":"We are seeking a highly skilled Generative AI Engineer to join our team and drive innovation in image and video restoration. You will work on state-of-the-art algorithms for deblurring, denoising, super-resolution, and related tasks, leveraging generative models such as diffusion models, GANs, and transformers. This role requires both strong research ability and practical engineering skills to develop scalable solutions for real-world applications.\nDescription\n- Research, design, and implement generative AI models for image and video restoration (e.g., deblurring, denoising, super-resolution, inpainting, frame interpolation).\n- Build and optimize training pipelines for large-scale datasets, including preprocessing, augmentation, and distributed training.\n- Evaluate restoration performance using both objective metrics (PSNR, SSIM, LPIPS) and subjective/perceptual quality measures.\n- Develop scalable and efficient inference pipelines, optimizing for latency, throughput, and memory.\n- Stay current with the latest research in computer vision and generative AI, and translate novel ideas into practical solutions.\n- Collaborate with cross-functional teams to integrate restoration models into production systems.\nMinimum Qualifications\nMaster’s or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.\nStrong background in computer vision and deep learning, with proven experience in generative models (diffusion, GANs, transformers).\nProficiency in Python and deep learning frameworks (PyTorch preferred).\nExperience with large-scale image/video datasets and distributed training (multi-GPU or multi-node).\nSolid understanding of image and video restoration metrics (PSNR, SSIM, LPIPS) and perceptual evaluation.\nHands-on experience with video preprocessing, such as motion estimation and optical flow, frame alignment and stabilization, temporal consistency techniques, and video encoding/decoding.\nStrong software engineering skills: clean code, Git, debugging, optimization.\nPreferred Qualifications\nTrack record of publications or open-source contributions in generative AI, computer vision, or image/video restoration.\nExperience with real-world video data processing (e.g., raw domain, HDR pipelines, ISP sharpening).\nFamiliarity with cloud-based large-scale dataset management (e.g., S3, distributed file systems).\nExperience with real-time or near-real-time video restoration and performance optimization.\nKnowledge of advanced motion analysis (scene change detection, temporal consistency checks, optical flow with RAFT/PWC-Net).\nFamiliarity with deployment on diverse hardware (edge devices, mobile, GPU acceleration).\nPractical experience with efficient model deployment, including model compression, quantization, distillation, and hardware optimization (e.g. TensorRT, mixed precision).","description_format":"text","description_chars":2849,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Operating 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